The hazards of helping: Work, mission and risk in non-profit social service organizations
Bibliographic record
Abstract
Non-profit organizations play an important role in the provision of health and social services. No longer temporary providers of emergency services, non-profit organizations appear to be permanent features of the social service landscape. Despite some of the intrinsic rewards that work in non-profit organizations offers, jobs in these organizations can be characterized by high demands, long working hours, low pay and exposure to violence and infectious disease, conditions which may be deleterious to worker health. This paper is based on an ethnography of three non-profit organizations: a homeless women's drop in, a drug treatment agency and a men's homeless shelter. We examine organizational ‘mission,’ a dominant discourse about the purpose and value of providing ‘help’ to marginalized clients, and the implications it has for work practices and for the way that workers understand work-related risk in these organizations. We describe how the notion of mission is continually reproduced, and trace its relationship to worker risk acceptance and risk taking. We suggest that the functions of such discursive commitments in organizations, and their implications for the well-being of workers, underscores the importance of understanding organizational culture and the social construction of risk when attempting to improve working conditions and protect worker health in social service non-profit organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".